The rise of connected and autonomous vehicles introduces significant cybersecurity challenges for embedded systems. One of the most vulnerable components is the Controller Area Network (CAN), which manages communication between a vehicle’s electronic units. Originally designed without built-in security mechanisms, the CAN bus is particularly susceptible to message injection attacks. This paper presents a hybrid anomaly detection framework that combines traditional models like Support Vector Machines (SVM) with recurrent neural networks such as Long Short-Term Memory (LSTM). We also evaluate additional algorithms, including Random Forest, Isolation Forest, and Autoencoders. The proposed architecture leverages the LSTM to extract temporal features from CAN traffic and uses the SVM for precise classification, balancing dynamic detection capabilities with real-time efficiency. Experimental results demonstrate that the hybrid model outperforms individual approaches in terms of precision, recall, F1-score, and Area Under the Curve (AUC), while also reducing false positive rates and increasing robustness. This makes the proposed framework a promising solution for enhancing the cybersecurity of modern in-vehicle networks.

Understanding driver behavior is essential for improving road safety and informing policy decisions. In this study, we use clustering techniques to classify drivers into behavioral groups based on personal attributes (such as date and place of birth), driving history (including traffic code violations), and license details (such as the age at which the license was obtained and the type of vehicle driven). Our approach applies KMeans clustering in a hierarchical manner to identify meaningful patterns within the data. We also used large language models to later facilitate interpretation of the clusters. And due to the large size and sensitive nature of the dataset, all processing is conducted within the organization’s secure data platform. The findings of this study could support the development of more effective traffic regulations, insurance models, and risk assessment strategies.

Multi-label classification presents a complex computational challenge with broad applications in text categorization, image annotation, and bioinformatics. In this paper, we introduce a pioneering approach that merges Deep Extra-Trees with three transformation methods to tackle this intricate task. Through comprehensive evaluations conducted on a range of benchmark datasets, we meticulously compare our method against established algorithms. The results not only validate our approach but also reveal its superiority, demonstrating enhanced performance and robustness. Our approach utilizes transformation methods of multi-class classification in conjunction with Deep Extra-Trees. Specifically, we implement Binary Relevance, Classifier Chains, and Label Powerset as our transformation methods, which effectively convert the multi-label problem into multiple single-label problems, thereby leveraging the power of Deep Extra-Trees for improved prediction accuracy. This substantiates the robustness and adaptability of our proposed methodology across diverse datasets. By offering a compelling solution to the multi-label classification problem, our research contributes significantly to the advancement of machine learning techniques in various domains. Moreover, we apply this approach not only to classification tasks but also to anomaly detection, further demonstrating its versatility and practical utility.

In the ever-evolving landscape of real-time usage, there is a constant pursuit of advancements in infrastructures and technologies to meet the growing demand for network accessibility across a wide range of services. In this context, the advent of 5G technology has brought about transformative changes in the realm of networking. 5G, the fifth generation of wireless communication technology, offers unprecedented speed, low latency, and massive connectivity. It has become a critical enabler for applications ranging from autonomous vehicles to the Internet of Things (IoT). The integration of 5G into the networking infrastructure introduces new dimensions to the challenges and opportunities associated with hypercube routing. One significant area of interest in this regard is the implementation of hypercube routing, which poses notable challenges. Researchers and professionals are actively exploring various methods and techniques to achieve precise computations within optimal time limits. This paper specifically focuses on a fundamental concept: the use of hypercubes in networking and their application as a routing solution. Additionally, the paper aims to investigate existing proposals and techniques that are relevant to addressing concerns like fault-tolerant routing. Furthermore, the paper introduces a novel scheme for routing in hypercube networks, incorporating a multicluster node, thereby presenting an innovative approach to tackle these challenges within the 5G ecosystem. Taking advantage of 5G capabilities, this scheme could potentially improve the efficiency and fault tolerance of hypercube routing in modern network infrastructures.

Medical prevention is a very important aspect of healthcare informatics research through the prediction of medical events (e.g., disease diagnosis). In this work, we propose a deep learning approach to perform multi-label prediction on acts of medical care and treatments. The proposed approach utilizes a time-aware long short-term memory network and extends it with additional information from a fuzzy clustering of the same portfolio. The former mechanism (time-aware) is used to handle the temporal irregularity between the elements of a medical trajectory whereas the latter mechanism (fuzzy clustering) assists in modeling the heterogeneity among patients and treatments. Using a large portfolio of reimbursed medical records (over 16 million consumed acts of medical care) by a healthcare insurance in France, we show that our approach outperforms traditional and deep learning methods in medical multi-label prediction. Our work has implications for supporting medical prevention and more broadly improving the quality of healthcare services and insurance.

Over the last decade, the demand for greater security in 5G networks has grown significantly. Ensuring data security during transmission against external attacks has become a critical priority. However, existing security systems, which focus on attack identification, face limitations in terms of both security and performance. This requires the implementation of more rigorous measures. Meeting the need for improved security in 5G networks calls for advanced machine learning techniques. To tackle this challenge, a proposed hybrid mechanism employs various machine learning approaches to effectively classify threats, such as denial of service, detection denial, and resource misuse. The incorporation of the DET model improves the accuracy of decision making and improves attack classification for 5G networks. Key accuracy parameters, including recall, precision, and F-score, play a crucial role in ensuring the model’s reliability. Simulation results demonstrate the superiority of the proposed model compared to others, particularly in terms of accuracy. Our approach presents a promising solution for identifying and categorizing attacks in 5G networks. By prioritizing accuracy and providing superior performance, this research significantly contributes to ongoing efforts to improve 5G network security.

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

In this paper, we propose an innovative architecture for anomaly detection in videos, motivated by the need to answer quickly to danger in monitoring streams, without requiring expensive computational power. Drawing inspiration from human behavior our approach integrates spatial and temporal analyses. For the temporal analysis, which classifies video sequences, we associate a recurrent convolutional network combining Visual Geometry Group Net 19 (VGG19) and Gated Reccurrent Units (GRU), with a Multilayer Perceptron (MLP). Simultaneously, the spatial analysis of individual images is conducted through You Only Look Once version 7 (YOLOv7). Then, both predictions are combined to perform the final prediction, where an anomaly is signaled if a perceived suspicious object or unexpected action occurs on the screen. Our experimental results shows the integration of both approaches reduces the rate of false negatives, leading to improved identification of anomalous events within video streams for both binary and multi-class models. We also show that multi-class models are less suited for this task than binary models.

In this paper, we propose a novel approach for building and initializing deep neural networks based on extremely randomized trees (extra-trees) an ensemble learning method for both classification and regression and feature extraction techniques. We use convolutional neural networks (CNNs), a family of modern deep learning models, extensively used in the area of computer vision and image classification, to improve the accuracy and generalization performance of classifiers. First, a CNN model is built to automatically extract multi-level features from the data. Second, a random forest obtains the structures of the trees. Finally, the neural networks (MLP) are built. This hybrid method combines two standard adaptive methods: decision trees and artificial neural networks. In this article, we illustrate the structure of the hybrid method, the problems occurring during the building of the model, and the solutions for these problems. The experimental results indicate that the proposed approach achieves consistently high performance for a variety of regression and classification tasks. These results should motivate further studies seeking to develop accurate and efficient tree-based models.

Anomaly detection is an active research field which attracts the attention of many business and research actors. It has led to several research projects depending on the nature of the data, the availability of labels on normality, and domains of application that are diverse such as fraud detection, medical domains, cloud monitoring or network intrusions detection, etc. However, dealing with effective anomaly detection for complex and high-dimensional time series data remains a challenging task. In this work, we propose hybrid approach composed of an LSTM Autoencoder trained on normal records to learn efficient normal sequence representations combined with an SVM classifier for anomaly detection. Experimental results show that by encoding time series via a pretrained LSTM encoder allows efficient representation of data so that we can accurately detect abnormal records. In fact, the encoded representation reduces significantly the correlations between normal and abnormal records and allows us to have an efficient latent data representation that separates consistently the two classes. The proposed hybrid approach outperforms state-of-the art approaches [1], [2], [3], [4].

Generative Topographic Mapping (GTM) is a popular probabilistic framework for modeling non-linear relationships in high-dimensional data as well as for unsupervised learning and visualization of such data. It is also known as to provide a principled probabilistic alternative to the well-known Self-Organizing Map (SOM) in the neural networks community, thanks to its flexible mixture model formulation and the desirable properties of the expectation-maximization (EM) algorithm. However, much attention has been focused on the use of GTM for multivariate data, in general assumed to be independent and identically distributed (i.i.d) and the problem of modeling sequences using GTM is less investigated. In this paper, we focus on GTM for unsupervised modeling and visualization of sequential data. We consider modeling sequences of continuous multidimensional observations and we propose a GTM through time (GTM-TT) approach based on hidden Markov models (HMM) where the observations are a sent of independent sequences, rather than a signle sequence. We further extend the model to the clustering of multiple sequences by proposing a GTM-TT mixture model. The model parameters are estimated by maximum likelihood via the EM algorithm. The proposed approach is evaluated using simulated data and real-world data.

We present a generative approach to train a new probabilistic self-organizing map (PrSOMS) for dependent and nonidentically distributed data sets. Our model defines a low-dimensional manifold allowing friendly visualizations. To yield the topology preserving maps, our model has the SOM like learning behavior with the advantages of probabilistic models. This new paradigm uses hidden Markov models (HMM) formalism and introduces relationships between the states. This allows us to take advantage of all the known classical views associated to topographic map. The objective function optimization has a clear interpretation, which allows us to propose expectation-maximization (EM) algorithm, based on the forward–backward algorithm, to train the model. We demonstrate our approach on two data sets: The real-world data issued from the "French National Audiovisual Institute" and handwriting data captured using a WACOM tablet.

This paper describes a new algorithm to learn a new probabilistic Self-Organizing Map for not independent and not identically distributed data set. This new paradigm probabilistic self-organizing map uses HMM (Hidden Markov Models) formalism and introduces relationships between the states of the map. The map structure is integrated in the parameter estimation of Markov model using a neighborhood function to learn a topographic clustering. We have applied this novel model to cluster and to reconstruct the data captured using a WACOM tablet.